Treadmill Customer Preference Insights for Cardio Good Fitness

Cardio Good Fitness is a retail store that among many products, sells treadmills.

The company has provided us with customer data of its treadmill products. We will use this data to build a customer profile for every treadmill product, and provide insights and recommendations on these that will help the company target new customers.

Figure 1 - AI Generated image

We will create a customer profile, perform univariate and multivariate analyses, and generate a set of insights and recommendations that will help the company in targeting new customers.

Data Dictionary

• Product - The model no. of the treadmill

• Age - Age of the customer in no of years

• Gender - Gender of the customer

• Education - Education of the customer in no. of years

• Marital Status - Marital status of the customer

• Usage - Avg. # times the customer wants to use the treadmill every week

• Fitness - Self rated fitness score of the customer (5 - very fit, 1 - very unfit)

• Income - Income of the customer

• Miles- Miles that a customer expects to run

Setup

#importing necessary libraries
  import pandas as pd
  import numpy as np
  import seaborn as sns
  import matplotlib.pyplot as plt

  #restrict float value to 3 decimal places
  pd.set_option('display.float_format',lambda x: '%.3f' % x)
  %matplotlib inline
  

#ignoring warnings
  import warnings
  warnings.filterwarnings("ignore")
  

#allow access to google drive
  from google.colab import drive
  drive.mount('/content/gdrive')
  

Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount("/content/gdrive", force_remount=True).
  

#import dataset and store into a dataframe
  cardiodata_df = pd.read_csv("/content/gdrive/MyDrive/Projects/ML/Data/CardioGoodFitness.csv")
  

---

Dataset

Figure 2 - Dataset Header as shown in Tableau

Attributes

Sanity Checks

#Understand the shape of the data
  cardiodata_df.shape
  

(180, 9)

print("The number or rows in the dataset is " + str(len(cardiodata_df.index)) + ". The number of columns in the dataset is " + str(len(cardiodata_df.columns)))
  print("There are " + str(len(cardiodata_df.index)) + " customers and " + str(len(cardiodata_df.columns)) + " features in the data set.")
  

The number or rows in the dataset is 180. The number of columns in the dataset is 9
  There are 180 customers and 9 features in the data set.
  

#Visualize the data types
  cardiodata_df.info()
  

<class 'pandas.core.frame.DataFrame'>
  RangeIndex: 180 entries, 0 to 179
  Data columns (total 9 columns):
   #   Column         Non-Null Count  Dtype
  ---  ------         --------------  -----
   0   Product        180 non-null    object
   1   Age            180 non-null    int64
   2   Gender         180 non-null    object
   3   Education      180 non-null    int64
   4   MaritalStatus  180 non-null    object
   5   Usage          180 non-null    int64
   6   Fitness        180 non-null    int64
   7   Income         180 non-null    int64
   8   Miles          180 non-null    int64
  dtypes: int64(6), object(3)
  memory usage: 12.8+ KB
  

There are 3 object type columns and 6 numerical columns. There are no NULL entries. The data types for each column are as expected.

#lets confirm if there are nulls in another way
  cardiodata_df.isnull().sum()
  

Product          0
  Age              0
  Gender           0
  Education        0
  MaritalStatus    0
  Usage            0
  Fitness          0
  Income           0
  Miles            0
  dtype: int64

The above corroborates that there are no null values.

#lets look for duplicate entries
  cardiodata_df.duplicated().sum()
  

0

There are no duplicated entries.

#Lets take a look at the first 5 lines
  cardiodata_df.head()
  

	Product	Age	Gender	Education	MaritalStatus	Usage	Fitness	Income	Miles
0	TM195	18	Male	14	Single	3	4	29562	112
1	TM195	19	Male	15	Single	2	3	31836	75
2	TM195	19	Female	14	Partnered	4	3	30699	66
3	TM195	19	Male	12	Single	3	3	32973	85
4	TM195	20	Male	13	Partnered	4	2	35247	47

#lets look at the last 15 lines
  cardiodata_df.tail(10)
  

	Product	Age	Gender	Education	MaritalStatus	Usage	Fitness	Income	Miles
170	TM798	31	Male	16	Partnered	6	5	89641	260
171	TM798	33	Female	18	Partnered	4	5	95866	200
172	TM798	34	Male	16	Single	5	5	92131	150
173	TM798	35	Male	16	Partnered	4	5	92131	360
174	TM798	38	Male	18	Partnered	5	5	104581	150
175	TM798	40	Male	21	Single	6	5	83416	200
176	TM798	42	Male	18	Single	5	4	89641	200
177	TM798	45	Male	16	Single	5	5	90886	160
178	TM798	47	Male	18	Partnered	4	5	104581	120
179	TM798	48	Male	18	Partnered	4	5	95508	180

#lets take a look at the statistics of the data set
  cardiodata_df.describe().T
  

	count	mean	std	min	25%	50%	75%	max
Age	180.000	28.789	6.943	18.000	24.000	26.000	33.000	50.000
Education	180.000	15.572	1.617	12.000	14.000	16.000	16.000	21.000
Usage	180.000	3.456	1.085	2.000	3.000	3.000	4.000	7.000
Fitness	180.000	3.311	0.959	1.000	3.000	3.000	4.000	5.000
Income	180.000	53719.578	16506.684	29562.000	44058.750	50596.500	58668.000	104581.000
Miles	180.000	103.194	51.864	21.000	66.000	94.000	114.750	360.000

Statistics of the dataset

• The average age of customers is 29. Fifty percent of customers are 26 or younger and 75% are 33 or less. The oldest person is 50. We anticipate that Cardio Good Fitness customers are young. No abnormal values observed.

• The average years of customer's education is 16 (College) and the minimum is 12 years (High School). Fifty percent of customers have 16 years of education or less. We anticipate that Cardio Good Fitness customers are well educated. No abnormal values observed.

• The average number of times a customer plans to use a treadmill is about 3 times a week, the minimum is twice a week and the maximum is every day of the week. No abnormal values observed.

• Regarding Fitness, most customers are relatively fit -at least average of better. No abnormal values observed.

• The average income is \$53,720, the minimum income is \$29,562 and the maximum is \$104,581. Less than 50% makes more than 51,000. No abnormal values observed.

• The average miles customers expect to run is 103, being the minimum 21 and the maximum 360. A question arises, Are these miles are going to be run during the lifetime of the treadmil, per month, per week, or per day? Can we assume that the miles are weekly miles? The maximum number of miles a customer intends to run is 360, which would amount to roughly over 51 miles per day, more than a marathon!. Lets analyze that further later.

We can conclude from the sanity check and dataset analysis:

• The dataset has no null values.
• The dataset has no duplicates.
• The dataset has no mixed data type values in a column.
• We will analyze outliers later.
• We need to make a conclusion regarding the feature Miles. Is this miles per week/month?

---

Univariate Data Analysis

Models (All Treadmills)

#How many Treadmill Products?
  product_lists_numbers = cardiodata_df['Product'].nunique()
  print("There are " + str(product_lists_numbers) + " treadmill products.")
  

There are 3 treadmill products.
  

#What are the Treadmill products
  products_model_list = cardiodata_df['Product'].unique()
  print("Cardio Good Fitness Treadmills Models are: ")
  print("Model")
  print("-----")
  for x in products_model_list:
    print(x)
  

Cardio Good Fitness Treadmills Models are:
  Model
  -----
  TM195
  TM498
  TM798
  

Figure 3 - Good Cardio Fitness Treadmill models

(reference only. Source: https://nymag.com/strategist/article/best-treadmills.html)

#lets declare a variable Figure to hold the figure number
  fig_no = 4 #first figure number
  

#Customer Preference (Quantity) per Product
  product_model_qty= cardiodata_df['Product'].value_counts()

  print("Model Quantity")
  print("--------------")
  print(product_model_qty)
  

Model Quantity
  --------------
  TM195    80
  TM498    60
  TM798    40
  Name: Product, dtype: int64
  

#Treadmill products visualization

  plt.title("Fig "+str(fig_no) + "- Customer Model Preferences",fontsize=15)
  plt.xlabel("Product")
  sns.countplot(data=cardiodata_df,x="Product")
  plt.show()
  fig_no+=1
  

#pie chart model preference
  labels_products = cardiodata_df['Product'].unique()
  plt.pie(data = cardiodata_df,x=product_model_qty, autopct="%.1f%%",labels=labels_products, pctdistance=0.5)
  plt.title("Fig "+ str(fig_no) + "- Customer Model Preferences", fontsize=15)
  plt.show()
  fig_no+=1
  

• The most prefered product is TM195 and TM798 is the least preferred.
• 44% of customers buy the TM195 model and 22% buy the TM798.
• There is rougly a 11.1% increment from TM798 to TM498, and also 11.1% from TM498 to TM195.

Age (All Treadmill Models)

#List all ages
  customer_age_list = cardiodata_df['Age'].unique()
  print("Cardio Good Fitness Customers Ages:")
  print(customer_age_list)
  

Cardio Good Fitness Customers Ages:
  [18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
   43 44 46 47 50 45 48 42]
  

#How many different ages in the customer's list
  cardiodata_df['Age'].nunique()
  

32

#How many customers per age?
  print("Age Num Cust")
  print("-"*14)
  cardiodata_df['Age'].value_counts().sort_index(ascending=True)
  

Age Num Cust
  --------------
  

18     1
  19     4
  20     5
  21     7
  22     7
  23    18
  24    12
  25    25
  26    12
  27     7
  28     9
  29     6
  30     7
  31     6
  32     4
  33     8
  34     6
  35     8
  36     1
  37     2
  38     7
  39     1
  40     5
  41     1
  42     1
  43     1
  44     1
  45     2
  46     1
  47     2
  48     2
  50     1
  Name: Age, dtype: int64

#Customer Age distribution
  #`stat` must be one of ['count', 'frequency', 'density', 'probability', 'proportion', 'percent']
  #histplot
  sns.histplot(data=cardiodata_df, x = "Age", stat="frequency")
  plt.title("Fig "+ str(fig_no) +"- Customer Age Distribution",fontsize=15)
  plt.show()
  fig_no+=1
  

The most popular customer age is 25.

#box plot age distribution
  plt.title('Fig '+str(fig_no)+'- Age Distribution (Boxplot)',fontsize=15)
  sns.axes_style('whitegrid')
  sns.boxplot(data=cardiodata_df, x="Age", color='red')
  plt.show()
  fig_no+=1
  

There are some outliers from 47 to 50 years old. But why would these outliers be wrong data? Customers aged 47-50 can perfectly use treadmill machines.

We consider these 'outliers' as good valuable data.

#just for visual purposes, we produce a histplot with a distribution curve (kde option)
  sns.set(style='darkgrid')
  sns.histplot(data=cardiodata_df, x = "Age", kde=True)
  plt.xlabel("Age")
  plt.ylabel("Number of Customers")
  plt.title("Fig "+str(fig_no)+"- Customer Age Distribution", fontdict={'fontsize':15})
  plt.show()
  fig_no+=1
  

We notice that the Age distributed is skewed to the right.

Lets group ages into bins:

• GroupAge1 : less than 20
• GroupAge2 : 21 to 30
• GroupAge3 : 31 to 40
• GroupAge4 : more than 40

#lets create 4 groups of ages to classify our customers
  label_age_bins=['-20','21-30','31-40','40+']
  print(label_age_bins)
  

['-20', '21-30', '31-40', '40+']
  

#create a column in the dataframe to hold the new bin
  cardiodata_df['Age_bins'] = pd.cut(cardiodata_df['Age'],bins=[0,20,30,40,50],labels=label_age_bins,right=True)
  

#Plot distribution of customers by age group bins
  plt.title("Fig "+str(fig_no)+"- Customer Distribution by Age groups (bins)",fontsize=15)
  sns.countplot(data=cardiodata_df, x = "Age_bins")
  plt.xlabel("Age Group")
  plt.show()
  fig_no+=1
  

#plot a pie to visualize the % per age group
  count_age_bins = cardiodata_df['Age_bins'].value_counts().sort_index(ascending=True)
  plt.pie(data=cardiodata_df, x = count_age_bins, autopct="%.1f%%", labels = label_age_bins, pctdistance=0.8)
  plt.title("Fig "+str(fig_no)+"- Customer Distribution by Age Group",fontsize=15)
  plt.show()
  fig_no+=1
  

#How many customers are younger than 30 years
  customers_younger_30 = len(cardiodata_df[cardiodata_df['Age']<30])
  print("There are ",customers_younger_30," customers younger than 30 years.")
  #what is the percentage of customers younger than 30?
  percentage_ageless30 = customers_younger_30*100/len(cardiodata_df.index)
  print("%.2f" % percentage_ageless30 + "% of customers are younger than 30.")
  

There are  113  customers younger than 30 years.
  62.78% of customers are younger than 30.
  

• Most customers are under 30.
• The largest age group is 21-30.

Gender (All Treadmill Models)

Good Cardio Fitness does not identify customers by more progressive genders.

#How many customers are identified in each gender
  customer_gender_qty = cardiodata_df['Gender'].value_counts()
  print("Cardio Good Fitness Customers Gender:")
  print("Gender  #Cust")
  print("------- -----")
  print(customer_gender_qty)
  

Cardio Good Fitness Customers Gender:
  Gender  #Cust
  ------- -----
  Male      104
  Female     76
  Name: Gender, dtype: int64
  

#Customer gender distribution
  plt.title("Fig "+str(fig_no)+"- Distribution of General Customers by Gender",fontsize=15)
  sns.countplot(data=cardiodata_df,x="Gender")
  plt.show()
  fig_no+=1
  

#Customer Gender Distribution
  count_gender = cardiodata_df['Gender'].value_counts().sort_index(ascending=True)
  label_gender = cardiodata_df['Gender'].sort_values(ascending=True).unique()
  plt.pie(data = cardiodata_df,x=count_gender, autopct="%.1f%%",labels=label_gender, pctdistance=0.5)
  gender_dist_title = "Distribution of General Customers by Gender"
  plt.title("Fig "+str(fig_no)+"- "+ gender_dist_title,fontsize=15)
  plt.show()
  fig_no+=1
  

• There are more male customers than female customers. 58% of customers are male.
• Good Cardio Fitness only consider Male and Female Genders.

Education (All Treadmill Models)

#Number of unique Education levels
  customer_education_unique = cardiodata_df['Education'].nunique()
  print("Number of Education Levels:" ,customer_education_unique)

  #How many customers are in each education level as categorized by years of education?
  educationcount_df = cardiodata_df['Education'].value_counts()
  print("Lvl #Cust")
  print("--- -----")
  print(educationcount_df)
  

Number of Education Levels: 8
  Lvl #Cust
  --- -----
  16    85
  14    55
  18    23
  15     5
  13     5
  12     3
  21     3
  20     1
  Name: Education, dtype: int64
  

#Analysis of Customers by Years of Education, pie chart
  #fig_no=13
  count_education_years = cardiodata_df['Education'].value_counts().sort_index(ascending=True)
  label_education_years = cardiodata_df['Education'].sort_values(ascending=True).unique()
  plt.title("Fig "+str(fig_no)+"- Distribution of Customers by Years of Education",fontsize=15, pad=64)
  plt.pie(data = cardiodata_df,x=count_education_years, autopct="%.1f%%",labels=label_education_years, pctdistance=0.85, radius=1.7)

  plt.show()
  fig_no+=1
  

#Visualization, Countplot and displot Years of Education distribution
  #fig_no=14
  fig, (ax1,ax2) = plt.subplots(1,2, figsize=(16,6))
  fig.suptitle("Fig "+str(fig_no)+"- Customers Distribution - Years of Education",fontsize=15)

  sns.countplot(data=cardiodata_df, x = "Education",ax=ax1)
  #plt.title("countplot", pad=1)
  #sns.displot(data=cardiodata_df, x = "Education", kind = 'kde', height=4, aspect=1.4,ax=ax2)
  sns.displot(data=cardiodata_df, x = "Education", ax=ax2)

  #plt.title("displot")
  #plt.show()
  fig_no+=1
  

Creating bins for Education

• 0 < Education <= 12 :High School
• 12 <Education <= 14 :Associate
• 14 < Education <= 17 :College
• 17 <Education <= 19 :Masters
• 19 <Education :PhD

#create the labels for the bins
  label_education_bins=['High School','Associate','College','Masters','PhD']
  print(label_education_bins)
  

['High School', 'Associate', 'College', 'Masters', 'PhD']
  

#store in dataframe
  cardiodata_df['Education_bins'] = pd.cut(cardiodata_df['Education'],bins=[0,12,14,17,19,24],labels=label_education_bins,right=True)
  

#creare countplot to visualize Education label bins
  plt.title("Fig "+str(fig_no)+"- Customer Distribution by Education Levels (bins)",fontsize=15)
  sns.countplot(data=cardiodata_df, x = "Education_bins")
  plt.xlabel("Education Level")
  plt.show()
  fig_no+=1
  

#create pie chart to visualize education distribution
  #fig_no=16
  count_education_bins = cardiodata_df['Education_bins'].value_counts().sort_index(ascending=True)
  plt.pie(data = cardiodata_df, x=count_education_bins, autopct="%.1f%%", labels = label_education_bins, pctdistance=0.8, radius=1.2)
  plt.title("Fig "+str(fig_no)+"- Distribution of Customers by Education Level",fontsize=15, pad=44)
  plt.show()
  fig_no+=1
  

#How many customers have at least some kind of college education
  count_education_college= cardiodata_df[cardiodata_df['Education']>12].value_counts()
  print("Most customers have some kind of college education: "+ str(len(count_education_college)) + " out of "+ str(len(cardiodata_df.index)))
  

Most customers have some kind of college education: 177 out of 180
  

#pie chart for college vs no college customers
  #fig_no=17
  #create bin
  label_college_bins=['No College','College']

  #store college_bins in dataframe
  cardiodata_df['college_bins'] = pd.cut(cardiodata_df['Education'],bins=[0,12,cardiodata_df['Education'].max()+1],labels=label_college_bins,right=True)
  count_college_bins = cardiodata_df['college_bins'].value_counts().sort_index(ascending=True)

  #create pie
  plt.pie(data = cardiodata_df, x=count_college_bins, autopct="%.1f%%", labels = label_college_bins, pctdistance=0.8, radius=1.2)
  plt.title("Fig "+str(fig_no)+"- Distribution of Customers by College Education",fontsize=15)
  plt.show()
  fig_no+=1
  

• There are a few customers with a PhD.
• Very few customers have only a High School Diploma.
• 50% of customers have a college degree
• Good Cardio Fitness customers are highly educated: 98.3% have some kind of college education.

Marital Status (All Treadmill Models)

#What kind of marital status are there?
  customer_maritalstatus_list =cardiodata_df['MaritalStatus'].unique()
  print("Cardio Good Fitness Customers' Marital Status are: ")
  print("Marital Status")
  print("-"*14)
  for x in customer_maritalstatus_list:
    print(x)
  

Cardio Good Fitness Customers' Marital Status are:
  Marital Status
  --------------
  Single
  Partnered
  

#How many customers in each marital status
  customer_maritalstatus_qty = cardiodata_df['MaritalStatus'].value_counts()
  print("Cardio Good Fitness Customers Gender:")
  print("MaritalStat No.Cust")
  print("-"*20)
  print(customer_maritalstatus_qty)
  

Cardio Good Fitness Customers Gender:
  MaritalStat No.Cust
  --------------------
  Partnered    107
  Single        73
  Name: MaritalStatus, dtype: int64
  

#Marital Status distribution countplot
  sns.countplot(data=cardiodata_df,x="MaritalStatus")
  plt.title("Fig "+str(fig_no)+"- Distribution of Customers by Marital Status",fontsize=15)
  plt.show()
  fig_no+=1
  

#Analysis of Customers by Marital Status, create a pie chart
  count_maritalstatus = cardiodata_df['MaritalStatus'].value_counts().sort_index(ascending=True)
  label_maritalstatus = cardiodata_df['MaritalStatus'].sort_values(ascending=True).unique()
  plt.pie(data = cardiodata_df,x=count_maritalstatus, autopct="%.1f%%",labels=label_maritalstatus, pctdistance=0.5)
  plt.title("Fig "+str(fig_no)+"- Distribution of Customers by Marital Status",fontsize=15)
  plt.show()
  fig_no+=1
  

• Most customers have a partner: 59% of customers have a partner.

Usage (All Treadmill Models)

cardiodata_df['Usage'].unique()
  

array([3, 2, 4, 5, 6, 7])

#Usage Distribution - histplot and displot
  #fig_no=20
  sns.histplot(data=cardiodata_df, x = "Usage", stat="frequency")
  plt.suptitle("Fig "+str(fig_no)+"- Customer Distribution - Usage per Week", fontsize=15)
  plt.title("histplot", pad=2)
  plt.show()
  sns.displot(data=cardiodata_df, x = "Usage", kind = 'kde', height=4, aspect=1.4)
  plt.title("displot")
  plt.show()
  fig_no+=1
  

#Analysis of Customers by Usage - pie chart
  #fig_no=21
  count_usage = cardiodata_df['Usage'].value_counts().sort_index(ascending=True)
  label_usage = cardiodata_df['Usage'].sort_values(ascending=True).unique()
  plt.pie(data = cardiodata_df,x=count_usage, autopct="%.1f%%",labels=label_usage, pctdistance=0.8, radius=1.2)
  plt.title("Fig "+str(fig_no)+"- Customers Distribution by Usage",fontsize=15)
  plt.show()
  fig_no+=1
  

• Most customers will use the treadmill betwen 3 and 4 times per week

Fitness (All Treadmill Models)

#this time we will make the title a variable
  title_fitness_degree_dist = "Customer Distribution by Degree of Fitness"
  

#Fitness Distribution visualization histplot with kde
  #fig_no=22
  sns.set(style='darkgrid')
  sns.histplot(data=cardiodata_df, x = "Fitness", kde=True)#.set(title = title_fitness_degree_dist)
  plt.xlabel("Fitness Degree")
  plt.ylabel("Number of Customers")
  plt.title("Fig "+str(fig_no)+"- "+title_fitness_degree_dist, fontsize=15, pad=22)
  plt.show()
  fig_no+=1
  

#visualize data as pie chart
  #fig_no=23
  count_fitness_degree = cardiodata_df['Fitness'].value_counts().sort_index(ascending=True)  #sort the index always alphabetically
  label_fitness_degree = cardiodata_df['Fitness'].sort_values(ascending=True).unique()  #sort the labels alphabetically
  plt.title("Fig "+str(fig_no)+"- "+title_fitness_degree_dist, fontsize=15)

  plt.pie(data=cardiodata_df,x=count_fitness_degree,autopct="%.1f%%", labels = label_fitness_degree, pctdistance=0.8)
  plt.axis('equal') #to ensure that pie is drawn as a circle
  plt.show()
  fig_no+=1
  

#Creating Bins for Degrees of Fitness
  #5 : Very Fit
  #4 : Fit
  #3 : Average
  #2 : Unfit
  #1 : Very unfit
  

#label bins
  label_fitness_bins=['Very Fit','Fit','Average','Unfit','Very Unfit']
  print(label_fitness_bins)
  

['Very Fit', 'Fit', 'Average', 'Unfit', 'Very Unfit']
  

#store bins in dataframe
  cardiodata_df['Fitness_bins'] = pd.cut(cardiodata_df['Fitness'],bins=[0,1,2,3,4,5],labels=label_fitness_bins,right=True)
  

#visualization Fitness - countplot
  #fig_no= 24
  plt.title("Fig "+str(fig_no)+"- Customer Distribution by Fitness Levels", fontsize=15, pad=22)
  sns.countplot(data=cardiodata_df, x = "Fitness_bins")
  plt.xlabel("Fitness Level")
  plt.show()
  fig_no+=1
  

#Visualize fitness level in a pie
  #fig_no = 25
  count_fitness_level = cardiodata_df['Fitness'].value_counts().sort_index(ascending=True)
  plt.pie(data=cardiodata_df,x=count_fitness_level,autopct="%.1f%%", labels = label_fitness_bins, pctdistance=0.7)
  plt.title("Fig "+str(fig_no)+"- Distrubution of Customers by Fitness Level",fontsize=15)
  plt.show()
  fig_no+=1
  

• More than half of the customers are Fit average or better.
• 30.5% of customers are Unfit/Very Unfit
• 1.1% are very fit

Income (All Treadmill Models)

#What are the different incomes customers report?
  cardiodata_df['Income'].unique()
  

array([ 29562,  31836,  30699,  32973,  35247,  37521,  36384,  38658,
          40932,  34110,  39795,  42069,  44343,  45480,  46617,  48891,
          53439,  43206,  52302,  51165,  50028,  54576,  68220,  55713,
          60261,  67083,  56850,  59124,  61398,  57987,  64809,  47754,
          65220,  62535,  48658,  54781,  48556,  58516,  53536,  61006,
          57271,  52291,  49801,  62251,  64741,  70966,  75946,  74701,
          69721,  83416,  88396,  90886,  92131,  77191,  52290,  85906,
         103336,  99601,  89641,  95866, 104581,  95508])

#number of unique income levels
  cardiodata_df['Income'].nunique()
  

62

#How many customers make at least 100,000/year?
  cardiodata_df.loc[cardiodata_df['Income']>100000]
  

	Product	Age	Gender	Education	MaritalStatus	Usage	Fitness	Income	Miles	Age_bins	Education_bins	college_bins	Fitness_bins
168	TM798	30	Male	18	Partnered	5	4	103336	160	21-30	Masters	College	Unfit
174	TM798	38	Male	18	Partnered	5	5	104581	150	31-40	Masters	College	Very Unfit
178	TM798	47	Male	18	Partnered	4	5	104581	120	40+	Masters	College	Very Unfit

#income distribution - visualization
  #fig_no = 26
  plt.suptitle("Fig "+str(fig_no)+"- Customer Distribution by Income")

  plt.title("histplot")
  sns.histplot(data=cardiodata_df, x = "Income", stat="frequency")
  plt.show()

  plt.title("boxplot")
  sns.boxplot(data=cardiodata_df, x = "Income")
  plt.show()

  sns.displot(data=cardiodata_df, x= "Income", height=4, aspect=1.4)
  plt.title("displot-kde")
  plt.show()

  plt.title("countplot")
  sns.countplot(data=cardiodata_df, x = "Income")
  plt.xticks(rotation=90)
  plt.show()

  fig_no+=1
  

#Lets group the incomes as:
  #up to 30,000 : -30K
  #from 30k - 50k : 30-50K
  #from 60K - 80K : 50-80K
  #from 80k - 100K : 80-100K
  #more than 100K : +100K
  

label_income_bins=['-30K','30-50K','50-80K','80-100K','+100K']
  print(label_income_bins)
  

['-30K', '30-50K', '50-80K', '80-100K', '+100K']
  

cardiodata_df['Income_bins'] = pd.cut(cardiodata_df['Income'],bins=[cardiodata_df['Income'].min(),30000,50000,80000,100000,cardiodata_df['Income'].max()],labels=label_income_bins,right=True)
  

#visualization - countplot
  #fig_no = 27
  plt.title("Fig "+str(fig_no)+"- Customer Distribution by Income Range", fontsize=15)
  sns.countplot(data=cardiodata_df, x = "Income_bins")
  plt.xlabel("Income Range")
  plt.show()
  fig_no+=1
  

#Visualization - pie chart %
  #fig_no = 28
  count_income_bins = cardiodata_df['Income_bins'].value_counts().sort_index(ascending=True)
  plt.pie(data=cardiodata_df,x=count_income_bins,autopct="%.1f%%", labels = label_income_bins, pctdistance=0.9, radius=1.6)
  plt.title("Fig "+str(fig_no)+"- Distribution of Customers by Income Range",fontsize=15, pad=84)
  plt.show()
  fig_no+=1
  

• Most customers have middle class and low middle class incomes.
• More than 85% of customers earn between 30 to 80K/year.
• Nobody earns less than 30K/year.

Miles (All Treadmill models)

#How many different miles customers claim to run
  cardiodata_df['Miles'].nunique()
  

37

#How about we just calculate the mode
  cardiodata_df['Miles'].mode()
  

0    85
  Name: Miles, dtype: int64

#List the unique number of miles customers intend to run
  #Are these miles per week, month, or lifetime of the treadmill?
  customer_miles = cardiodata_df['Miles'].unique()
  print(customer_miles)
  

[112  75  66  85  47 141 103  94 113  38 188  56 132 169  64  53 106  95
   212  42 127  74 170  21 120 200 140 100  80 160 180 240 150 300 280 260
   360]
  

#Visualization Miles Distribution plots
  #fig_no = 29

  sns.histplot(data=cardiodata_df, x = "Miles", stat="frequency")
  plt.suptitle("Fig "+str(fig_no)+"- Customer Distribution by Miles to run", fontsize=15)
  plt.title("histplot", pad=2)
  plt.show()

  sns.boxplot(data=cardiodata_df, x = "Miles")
  plt.title("boxplot")
  plt.show()

  sns.displot(data=cardiodata_df,x = "Miles", kind='kde',height=4, aspect=1.4)
  plt.title("displot")
  plt.show()

  sns.countplot(data=cardiodata_df, x = "Miles")
  plt.title("countplot")
  plt.xticks(rotation=90)
  plt.show()

  fig_no+=1
  

The feature Miles are the number of miles that customers intend to use their treadmills. However, it is not clear if these are miles per day, week, month, or for the lifetime of the treadmill.

According to Mark Sains, who has run 80+ mile training weeks for marathons (https://qr.ae/pvlOBc), roughly, the peak weekly mileage for marathon runners will tend to be about:

• 90–120 miles for elites
• 70–100 miles for fast club runners
• 40–70 miles for club runners
• 30–50 miles for one-off marathon runners

Lets try to find out.

#Data from Mark Sains
  EliteMilesWeekLow = 90
  EliteMilesWeekHigh = 120
  FastClubRunnersWeekLow = 70
  FastClubRunnersWeekHigh = 100
  ClubRunnersWeekLow = 40
  ClubRunnersWeekHigh = 70
  OneTimeMarathonWeekLow = 30
  OneTimeMarathonWeekHigh = 50

  #How many customers pretend to run a particular amount of miles?
  MaxMiles = cardiodata_df['Miles'].max()
  print("Maximum Declared number of miles: ", MaxMiles)
  MinMiles = cardiodata_df['Miles'].min()
  print("Minimum Declared number of miles: ", MinMiles)
  AverageMiles = cardiodata_df['Miles'].median()
  print("Median Declared number of miles: ", AverageMiles)
  ModeMiles = cardiodata_df['Miles'].mode()
  print("Mode Declared number of miles: ", ModeMiles)
  

Maximum Declared number of miles:  360
  Minimum Declared number of miles:  21
  Median Declared number of miles:  94.0
  Mode Declared number of miles:  0    85
  Name: Miles, dtype: int64
  

#Could the declared number of miles be per day?
  MilesWeekMax = MaxMiles*7
  MilesWeekMin = MinMiles*7
  MilesWeekAvg = AverageMiles*7
  print("Miles Declared are per Day?")
  print("-"*28)
  print("Maximum/Week   miles %d" % MilesWeekMax)
  print("Average/Week   miles %d" % MilesWeekAvg)
  print("Minimum/Week   miles %d" % MilesWeekMin)
  print("Number of miles run per Week")

  if MilesWeekMax > EliteMilesWeekHigh :
    print("Miles declared are not per day. If per day, there would be a customer running %d miles per week!" % MilesWeekMax)
    print("That would mean that the customer will run "+ str(MilesWeekMax - EliteMilesWeekHigh) + " more miles than an Elite World Runner in a period of one week!\n")
  else:
    print("Miles declared could be per day\n")

  #Could the declared number of miles be per week?
  MilesWeekMax = MaxMiles
  MilesWeekMin = MinMiles
  MilesWeekAvg = AverageMiles
  print("Miles Declared are per Week?")
  print("-"*28)
  print("Maximum/Week   miles %d" % MilesWeekMax)
  print("Average/Week   miles %d" % MilesWeekAvg)
  print("Minimum/Week   miles %d" % MilesWeekMin)
  print("Number of miles run per Week.")

  if MilesWeekMax > EliteMilesWeekHigh :
    print("Miles declared are not per week. If per week, there would be a customer running %d miles per week!" % MilesWeekMax)
    print("That would mean that the customer will run "+ str(MilesWeekMax - EliteMilesWeekHigh) + " more miles than an Elite World Runner in a period of one week!\n")
  else:
    print("Miles declared could be per week.\n")

  #Could the declared number of miles be per month?
  MilesWeekMax = MaxMiles/4.3
  MilesWeekMin = MinMiles/4.3
  MilesWeekAvg = AverageMiles/4.3
  print("Miles Declared are per Month?")
  print("-"*28)
  print("Maximum/Week   miles %.2f" % MilesWeekMax)
  print("Average/Week   miles %.2f" % MilesWeekAvg)
  print("Minimum/Week   miles %.2f" % MilesWeekMin)
  print("Number of miles run per Week.")

  if MilesWeekMax > EliteMilesWeekHigh :
    print("Miles declared are not per month. If per month, there would be a customer running %.2f miles per week!" % MilesWeekMax)
    print("That would mean that the customer will run "+ str(MilesWeekMax - EliteMilesWeekHigh) + " more miles than an Elite World Runner in a period of one week!\n")
  else:
    print("Miles declared could be per month.\n")

  #Could the declared number of miles be per Year?
  MilesWeekMax = MaxMiles/52
  MilesWeekMin = MinMiles/52
  MilesWeekAvg = AverageMiles/52
  print("Miles Declared are per Year?")
  print("-"*28)
  print("Maximum/Week   miles %.2f" % MilesWeekMax)
  print("Average/Week   miles %.2f" % MilesWeekAvg)
  print("Minimum/Week   miles %.2f" % MilesWeekMin)
  print("Number of miles run per Week.")

  if MilesWeekMax > EliteMilesWeekHigh :
    print("Miles declared are not per Year. If per month, there would be a customer running %.2f miles per week!" % MilesWeekMax)
    print("That would mean that the customer will run "+ str(MilesWeekMax - EliteMilesWeekHigh) + " more miles than an Elite World Runner in a period of one week!\n")
  else:
    print("Miles declared could be per Year.\n")
  

Miles Declared are per Day?
  ----------------------------
  Maximum/Week   miles 2520
  Average/Week   miles 658
  Minimum/Week   miles 147
  Number of miles run per Week
  Miles declared are not per day. If per day, there would be a customer running 2520 miles per week!
  That would mean that the customer will run 2400 more miles than an Elite World Runner in a period of one week!

  Miles Declared are per Week?
  ----------------------------
  Maximum/Week   miles 360
  Average/Week   miles 94
  Minimum/Week   miles 21
  Number of miles run per Week.
  Miles declared are not per week. If per week, there would be a customer running 360 miles per week!
  That would mean that the customer will run 240 more miles than an Elite World Runner in a period of one week!

  Miles Declared are per Month?
  ----------------------------
  Maximum/Week   miles 83.72
  Average/Week   miles 21.86
  Minimum/Week   miles 4.88
  Number of miles run per Week.
  Miles declared could be per month.

  Miles Declared are per Year?
  ----------------------------
  Maximum/Week   miles 6.92
  Average/Week   miles 1.81
  Minimum/Week   miles 0.40
  Number of miles run per Week.
  Miles declared could be per Year.

  

From the above analysis, we can conclude that the customers Miles are per month.

We will implement bins for miles using Mark Sains categorization and add lower categories.

• 90–120 x 4.3 miles/month for elites
• 70–90 x 4.3 miles/month for fast club runners
• 50–70 x 4.3 miles/month for club runners
• 30–50 x 4.3 miles/month for one-off marathon runners
• 20-30 x 4.3 miles/month for determined
• 10-20 x 4.3 miles/month for intermediate
• 5-10 x 4.3 miles/month for enthusiast
• 2.5-5 x 4.3 for beginners
• 0-2.5 x 4.3 miles for ultra beginners

#bin labels
  label_miles_bins=['Ultra Beginner', 'Beginner', 'Enthusiast', 'Intermediate', 'Determined', 'One-Off Marathon', 'Club', 'Fast Club', 'Elite']
  print(label_miles_bins)
  

['Ultra Beginner', 'Beginner', 'Enthusiast', 'Intermediate', 'Determined', 'One-Off Marathon', 'Club', 'Fast Club', 'Elite']
  

#store in dataframe
  cardiodata_df['Miles_bins'] = pd.cut(cardiodata_df['Miles'],bins=[0, 2.5*4.3, 5*4.3, 10*4.3, 20*4.3, 30*4.3, 50*4.3, 70*4.3, 90*4.3,130*4.3],labels=label_miles_bins,right=True)
  

#visualization Distribution of runner categories
  #fig_no =30

  plt.title("Fig "+str(fig_no)+"- Customer Distribution by Runner Category", fontsize=15)
  sns.countplot(data=cardiodata_df, y = "Miles_bins")
  plt.xlabel("count")
  plt.ylabel("Runner Category")
  plt.show()

  fig_no+=1
  

#How many customers in each category
  cardiodata_df['Miles_bins'].value_counts()
  

Intermediate        79
  Determined          56
  One-Off Marathon    32
  Enthusiast           7
  Club                 4
  Beginner             1
  Fast Club            1
  Ultra Beginner       0
  Elite                0
  Name: Miles_bins, dtype: int64

#Visualization pie chart - Distribution of runners
  #fig_no = 31

  count_miles_bins = cardiodata_df['Miles_bins'].value_counts().sort_index(ascending=True)
  plt.pie(data=cardiodata_df,x=count_miles_bins,autopct="%.1f%%", labels = label_miles_bins, pctdistance=0.8, radius=2.8)
  plt.title("Fig "+str(fig_no)+"- Distribution of Customers by Runner Category (miles to run)",fontsize=15, pad=184)
  plt.show()
  fig_no+=1
  

• Most Customers (44%) fall into the category of "Intermediate" Level, those who run 10-20 miles per month.
• There is a large group (31%) of "determined" runners who intend to run 20-30 miles per month.
• About 18% of customers intend to run 30-50 miles per month, and are so called "one-time marathon runners".
• There is only 1 Fast Club runner.
• There is only 1 Ultra Beginning runner.
• There are no Elite runners.

Bivariate Analysis

Lets start with a correlation analysis.

The first issue is that we have 3 features that are categorical values. Therefore we should convert them now to numerical.

Lets convert:

• Gender (Male = 0, Female = 1)
• MaritalStatus (Single = 0, Partnered = 1)
• Model (TM195=1, TM498 = 2, TM798= 3)

Changing Categories into Numbers

#copy the dataframe to another
  cardiodata_ndf = cardiodata_df

  #change categorical Gender to numerical
  cardiodata_ndf['Gender'].replace(['Male','Female'],[0,1], inplace=True)

  #change categorical MaritalStatus to numerical
  cardiodata_ndf['MaritalStatus'].replace(['Single','Partnered'],[0,1], inplace=True)

  #change categorical Product to numerical
  cardiodata_ndf['Product'].replace(['TM195','TM498','TM798'],[0, 1, 2], inplace=True)

  #lets see the datatypes now
  cardiodata_ndf.info()
  

<class 'pandas.core.frame.DataFrame'>
  RangeIndex: 180 entries, 0 to 179
  Data columns (total 15 columns):
   #   Column          Non-Null Count  Dtype
  ---  ------          --------------  -----
   0   Product         180 non-null    int64
   1   Age             180 non-null    int64
   2   Gender          180 non-null    int64
   3   Education       180 non-null    int64
   4   MaritalStatus   180 non-null    int64
   5   Usage           180 non-null    int64
   6   Fitness         180 non-null    int64
   7   Income          180 non-null    int64
   8   Miles           180 non-null    int64
   9   Age_bins        180 non-null    category
   10  Education_bins  180 non-null    category
   11  college_bins    180 non-null    category
   12  Fitness_bins    180 non-null    category
   13  Income_bins     179 non-null    category
   14  Miles_bins      180 non-null    category
  dtypes: category(6), int64(9)
  memory usage: 15.1 KB
  

We can see that we changed features Product, Gender, and MaritalStatus to numerical.

#lets run stat
  cardiodata_ndf.describe().T
  

	count	mean	std	min	25%	50%	75%	max
Product	180.000	0.778	0.788	0.000	0.000	1.000	1.000	2.000
Age	180.000	28.789	6.943	18.000	24.000	26.000	33.000	50.000
Gender	180.000	0.422	0.495	0.000	0.000	0.000	1.000	1.000
Education	180.000	15.572	1.617	12.000	14.000	16.000	16.000	21.000
MaritalStatus	180.000	0.594	0.492	0.000	0.000	1.000	1.000	1.000
Usage	180.000	3.456	1.085	2.000	3.000	3.000	4.000	7.000
Fitness	180.000	3.311	0.959	1.000	3.000	3.000	4.000	5.000
Income	180.000	53719.578	16506.684	29562.000	44058.750	50596.500	58668.000	104581.000
Miles	180.000	103.194	51.864	21.000	66.000	94.000	114.750	360.000

Lets see a tail and a head

cardiodata_ndf.head()
  

	Product	Age	Gender	Education	MaritalStatus	Usage	Fitness	Income	Miles	Age_bins	Education_bins	college_bins	Fitness_bins	Income_bins	Miles_bins
0	0	18	0	14	0	3	4	29562	112	-20	Associate	College	Unfit	NaN	Determined
1	0	19	0	15	0	2	3	31836	75	-20	College	College	Average	30-50K	Intermediate
2	0	19	1	14	1	4	3	30699	66	-20	Associate	College	Average	30-50K	Intermediate
3	0	19	0	12	0	3	3	32973	85	-20	High School	No College	Average	30-50K	Intermediate
4	0	20	0	13	1	4	2	35247	47	-20	Associate	College	Fit	30-50K	Intermediate

cardiodata_ndf.tail()
  

	Product	Age	Gender	Education	MaritalStatus	Usage	Fitness	Income	Miles	Age_bins	Education_bins	college_bins	Fitness_bins	Income_bins	Miles_bins
175	2	40	0	21	0	6	5	83416	200	31-40	PhD	College	Very Unfit	80-100K	One-Off Marathon
176	2	42	0	18	0	5	4	89641	200	40+	Masters	College	Unfit	80-100K	One-Off Marathon
177	2	45	0	16	0	5	5	90886	160	40+	College	College	Very Unfit	80-100K	One-Off Marathon
178	2	47	0	18	1	4	5	104581	120	40+	Masters	College	Very Unfit	+100K	Determined
179	2	48	0	18	1	4	5	95508	180	40+	Masters	College	Very Unfit	80-100K	One-Off Marathon

Now lets run a correlation analysis

Correlation Analysis

#Bivariate analysis
  #fig_no = 32
  plt.figure(figsize = (10,5))
  plt.suptitle("Fig "+str(fig_no)+ "- Correlation Between features", fontsize=15)

  plt.title("Confusion Matrix (Heatmap)", pad=2)
  sns.heatmap(cardiodata_ndf.corr(),annot=True)
  plt.show()
  fig_no+=1
  

High Correlation (+/- 1 - +/- 0.5)

From the Confucious matrix we can observe the following:

Product - Education (0.50)

Product - Usage (0.54)

Product - Fitness (0.59)

Product - Income (0.62)

Product - Miles (0.57)

Income - Age (0.51)

Income - Education (0.63)

Income - Usage (0.52)

Income - Fitness (0.54)

Income - Miles (0.54)

Miles - Usage (0.76)

Miles - Fitness (0.79)

Fitness - Usage (0.67)

Medium Correlation (+/-0.3 - +/-0.49)

Miles - Education (0.31)

Fitness - Education (0.41)

Usage - Education (0.4)

Low Correlation (below +/- 0.29)

Miles - Age (0.037)

Fitness - Age (0.061)

Usage - Age (0.015)

Education - Age (0.28)

Product - Age (0.032)

Product - Gender (-0.23)

Product - MaritalStatus (-0.018)

MaritalStatus - Age (0.19)

MaritalStatus - Gender (0.019)

MaritalStatus - Education (0.069)

MaritalStatus - Usage (-0.0078)

MaritalStatus - Fitness (-0.051)

MaritalStatus - Income (0.15)

MaritalStatus - Miles (0.026)

Gender - Age (-0.028)

Gender - Education (-0.094)

Gender - MaritalStatus (0.019)

Gender - Usage (-0.21)

Gender - Fitness (-0.25)

Gender - Income (-0.2)

Gender - Miles (-0.22)

Income vs Age

From the Heatmap we see that Income and age have a high Correlation of 0.51.

cardiodata_ndf[['Age','Income']].corr()
  

	Age	Income
Age	1.000	0.513
Income	0.513	1.000

#scatter plot and lmplot to graphically visualize correlation
  #fig_no=33

  sns.scatterplot(data = cardiodata_ndf, x = 'Age',y='Income')
  plt.suptitle("Fig "+str(fig_no)+"- Age vs Income", fontsize=15)
  plt.title("scatterplot")
  plt.show()

  sns.lmplot(data=cardiodata_ndf, x='Age',y="Income", height=4, aspect=1.5)
  plt.title("regplot()+FacetGrid = lmplot")
  plt.xlim(cardiodata_ndf['Age'].min()-1,cardiodata_ndf['Age'].max()+1)
  plt.show()

  fig_no+=1
  

We can see that Income and Age are highly correlated, that is, the older the customer, in general the higher the income.

Income vs Education

From the heatmap we notice a high correlation of 0.626.

cardiodata_ndf[['Education','Income']].corr()
  

	Education	Income
Education	1.000	0.626
Income	0.626	1.000

#scatter plot and lmplot to graphically visualize correlation
  #fig_no = 34
  sns.scatterplot(data = cardiodata_ndf, x = 'Education',y='Income')
  plt.suptitle("Fig "+ str(fig_no)+"- Education vs Income", fontsize = 15)
  plt.title("scatterplot")
  plt.show()

  sns.lmplot(data=cardiodata_ndf, x='Education',y="Income", height=4, aspect=1.5)
  plt.title("regplot()+FacetGrid = lmplot")
  plt.xlim(cardiodata_ndf['Education'].min()-1,cardiodata_ndf['Education'].max()+1)
  plt.show()

  fig_no+=1
  

We can corroborate that Education and Income are higly related. That makes sense, people with more education would probably have a larger income than people with less education.

Income vs Usage

From the Heatmap we see that Income and Usage have a high Correlation 0.52

cardiodata_ndf[['Usage','Income']].corr()
  

	Usage	Income
Usage	1.000	0.520
Income	0.520	1.000

#common variables
  maintitle = "Income vs Usage"
  subtitle_scatter = "Scatter Plot"
  subtitle_lmplot = "regplot()+FacetGrid = lmplot"
  

#scatter plot and lmplot to graphically visualize correlation
  #fig_no = 35

  sns.scatterplot(data = cardiodata_ndf, x = 'Usage',y='Income')
  plt.suptitle("Fig "+str(fig_no)+"- "+maintitle,fontsize=15)
  plt.title(subtitle_scatter)
  plt.show()

  sns.lmplot(data=cardiodata_ndf, x='Usage',y="Income", height=4, aspect=1.5)
  plt.title(subtitle_lmplot)
  plt.xlim(cardiodata_df['Usage'].min()-1,cardiodata_ndf['Usage'].max()+1)
  plt.show()

  fig_no+=1
  

Income and Usage are highly correlated. That is probably because people with larger incomes can have more time to use the treadmill.

Income vs Fitness

From the Heatmap we see that Income and Fitness have a high Correlation 0.54

cardiodata_ndf[['Fitness','Income']].corr()
  

	Fitness	Income
Fitness	1.000	0.535
Income	0.535	1.000

#scatter plot and lmplot to graphically visualize correlation

  maintitle = "Fitness vs Income"
  #fig_no = 36

  sns.scatterplot(data = cardiodata_ndf, x = 'Fitness',y='Income')
  plt.suptitle("Fig "+str(fig_no)+"- "+ maintitle,fontsize=15)
  plt.title(subtitle_scatter)
  plt.show()

  sns.lmplot(data=cardiodata_ndf, x='Fitness',y="Income", height=4, aspect=1.5)
  plt.title(subtitle_lmplot)
  plt.xlim(cardiodata_df['Fitness'].min()-1,cardiodata_ndf['Fitness'].max()+1)
  plt.show()

  fig_no +=1
  

People with larger incomes are more fit. Maybe because as we noticed earlier, people with larger incomes can use their treadmills more times than people with smaller incomes.

Income vs Miles

From the Heatmap we see that Income and Miles have a high Correlation 0.54

cardiodata_ndf[['Miles','Income']].corr()
  

	Miles	Income
Miles	1.000	0.543
Income	0.543	1.000

#scatter plot and lmplot to graphically visualize correlation
  maintitle = "Miles vs Income"
  #fig_no = 37
  sns.scatterplot(data = cardiodata_ndf, x = 'Miles',y='Income')
  plt.suptitle("Fig "+str(fig_no)+"- "+maintitle,fontsize=15)
  plt.title(subtitle_scatter)
  plt.show()

  sns.lmplot(data=cardiodata_ndf, x='Miles',y="Income", height=4, aspect=1.5)
  plt.xlim(cardiodata_ndf['Miles'].min()-1,cardiodata_ndf['Miles'].max()+1)
  plt.title(subtitle_lmplot)
  plt.show()
  

Income and Miles are highly correlated. This shows that people with larger incomes generally run more miles. This may be because customers with larger incomes have more time to use their treadmills.

Miles vs Usage

From the Heatmap we see that Miles and Usage have a high Correlation 0.76

cardiodata_ndf[['Miles','Usage']].corr()
  

	Miles	Usage
Miles	1.000	0.759
Usage	0.759	1.000

#scatter plot and lmplot to graphically visualize correlation

  maintitle = "Miles vs Usage"
  #fig_no = 38

  sns.scatterplot(data = cardiodata_ndf, x = 'Miles',y='Usage')
  plt.suptitle("Fig "+str(fig_no)+"- "+maintitle,fontsize=15)
  plt.title(subtitle_scatter)
  plt.show()

  sns.lmplot(data=cardiodata_ndf, x='Miles',y="Usage", height=4, aspect=1.4)
  plt.xlim(cardiodata_ndf['Miles'].min()-1,cardiodata_ndf['Miles'].max()+1)
  plt.title(subtitle_lmplot)
  plt.show()

  fig_no+=1
  

This shows that miles and usage are highly correlated. This is clear: the more times a customer uses a treadmill the more miles the customer will run.

Miles vs Fitness

From the Heatmap we see that Miles and Fitness have a high Correlation 0.79

cardiodata_ndf[['Miles','Fitness']].corr()
  

	Miles	Fitness
Miles	1.000	0.786
Fitness	0.786	1.000

#scatter plot and lmplot to graphically visualize correlation
  maintitle = "Miles vs Fitness"
  #fig_no = 39

  sns.scatterplot(data = cardiodata_ndf, x = 'Miles',y='Fitness')
  plt.suptitle("Fig "+ str(fig_no)+"- "+maintitle,fontsize=15)
  plt.title(subtitle_scatter)
  plt.show()

  sns.lmplot(data=cardiodata_ndf, x='Miles',y="Fitness", height=4, aspect=1.4)
  plt.xlim(cardiodata_ndf['Miles'].min()-1,cardiodata_ndf['Miles'].max()+1)
  plt.title(subtitle_lmplot)
  plt.show()

  fig_no+=1
  

This one is also very clear. The more miles a customer runs the more fit he/she will be.

Fitness vs Usage

From the Heatmap we see that Fitness and Usage have a high Correlation 0.67

cardiodata_ndf[['Fitness','Usage']].corr()
  

	Fitness	Usage
Fitness	1.000	0.669
Usage	0.669	1.000

#scatter plot and lmplot to graphically visualize correlation
  maintitle = "Usage vs Fitness"
  #fig_no = 40

  sns.scatterplot(data = cardiodata_ndf, x = 'Usage',y='Fitness')
  plt.suptitle("Fig "+str(fig_no)+"- "+maintitle,fontsize=15)
  plt.title(subtitle_scatter)
  plt.show()

  sns.lmplot(data=cardiodata_ndf, x='Usage',y="Fitness", height=4, aspect=1.4)
  plt.xlim(cardiodata_ndf['Usage'].min()-1,cardiodata_ndf['Usage'].max()+1)
  plt.title(subtitle_lmplot)
  plt.show()

  fig_no+=1
  

This is very clear. The more times the treadmill will be used, the more fit the customer will be.

Miles vs Education

From the Heatmap we see that Income and Usage have a moderate Correlation 0.31

cardiodata_ndf[['Education','Miles']].corr()
  

	Education	Miles
Education	1.000	0.307
Miles	0.307	1.000

#scatter plot and lmplot to graphically visualize correlation
  maintitle = "Miles vs Education"
  #fig_no = 41

  sns.scatterplot(data = cardiodata_ndf, x = 'Miles',y='Education')
  plt.suptitle("Fig "+str(fig_no)+"- "+maintitle,fontsize=15)
  plt.title(subtitle_scatter)
  plt.show()

  sns.lmplot(data=cardiodata_ndf, x='Miles',y="Education", height=4, aspect=1.4)
  plt.xlim(cardiodata_ndf['Miles'].min()-1,cardiodata_df['Miles'].max()+1)
  plt.title(subtitle_lmplot)
  plt.show()

  fig_no+=1
  

This plot shows a moderate correlation. The more education a customer has, the more miles he/she will run. This is probably because a customer with more education will have a larger income than customers with less education, and have more time to use the treadmill which will result in more miles ran.

Fitness vs Education

From the Heatmap we see that Education and Fitness have a moderate Correlation 0.41

cardiodata_ndf[['Education','Fitness']].corr()
  

	Education	Fitness
Education	1.000	0.411
Fitness	0.411	1.000

#scatter plot and lmplot to graphically visualize correlation
  maintitle = "Education vs Fitness"
  #fig_no=42

  sns.scatterplot(data = cardiodata_df, x = 'Education',y='Fitness')
  plt.suptitle("Fig "+str(fig_no)+"- "+maintitle,fontsize=15)
  plt.title(subtitle_scatter)
  plt.show()

  sns.lmplot(data=cardiodata_df, x='Education',y="Fitness", height=4, aspect=1.4)
  plt.xlim(cardiodata_df['Education'].min()-1,cardiodata_df['Education'].max()+1)
  plt.title(subtitle_lmplot)
  plt.show()

  fig_no+=1
  

This shows a medium correlation. Probably with more education a customer will have a higher income than with less education, thus having more time to use the treadmill resulting in more fitness.

Usage vs Education

From the heatmap we see a moderate correlation of 0.395.

cardiodata_ndf[['Education','Usage']].corr()
  

	Education	Usage
Education	1.000	0.395
Usage	0.395	1.000

#scatter plot and lmplot to graphically visualize correlation
  maintitle = "Education vs Usage"
  #fig_no = 43

  sns.scatterplot(data = cardiodata_ndf, x = 'Education',y='Usage')
  plt.suptitle("Fig "+str(fig_no)+"- "+maintitle,fontsize=15)
  plt.title(subtitle_scatter)
  plt.show()

  sns.lmplot(data=cardiodata_ndf, x='Education',y="Usage", height=4, aspect=1.4)
  plt.xlim(cardiodata_ndf['Education'].min()-1,cardiodata_ndf['Education'].max()+1)
  plt.title(subtitle_lmplot)
  plt.show()

  fig_no+=1
  

Medium correlation that means that with more education, a customer can use the treadmill more times. This may be because with more education, a customer may have a higher income than with less education, resulting in being able to use the treadmill more times.

Miles vs Age

From the heatmap we see a low correlation of 0.037 between Miles and Age.

cardiodata_ndf[['Miles','Age']].corr()
  

	Miles	Age
Miles	1.000	0.037
Age	0.037	1.000

#scatter plot and lmplot to graphically visualize correlation
  maintitle = "Miles vs Age"
  #fig_no = 44

  sns.scatterplot(data = cardiodata_ndf, x = 'Miles',y='Age')
  plt.suptitle("Fig "+str(fig_no)+"- "+maintitle,fontsize=15)
  plt.title(subtitle_scatter)
  plt.show()

  sns.lmplot(data=cardiodata_ndf, x='Miles',y="Age", height=4, aspect=1.4)
  plt.xlim(cardiodata_ndf['Miles'].min()-1,cardiodata_ndf['Miles'].max()+1)
  plt.title(subtitle_lmplot)
  plt.show()

  fig_no+=1
  

Low correlation. Age doesn't seem to be determinant on the number of miles a customer intends to run.

Fitness vs Age

The heatmap indicates a low correlation between Fitness and Age of 0.061

#correlation between Age and Fitness
  cardiodata_ndf[['Age','Fitness']].corr()
  

	Age	Fitness
Age	1.000	0.061
Fitness	0.061	1.000

#scatter plot and lmplot to graphically visualize correlation
  maintitle = "Age vs Fitness"
  #fig_no = 45

  sns.scatterplot(data = cardiodata_ndf, x = 'Age',y='Fitness')
  plt.suptitle("Fig "+str(fig_no)+"- "+maintitle,fontsize=15)
  plt.title(subtitle_scatter)
  plt.show()

  sns.lmplot(data=cardiodata_ndf, x='Age',y="Fitness", height=4, aspect=1.4)
  plt.title(subtitle_lmplot)
  plt.xlim(15,50)
  plt.xlim(cardiodata_ndf['Age'].min()-1,cardiodata_ndf['Age'].max()+1)
  plt.show()

  fig_no+=1
  

The plot shows a low correlation. The age is not determinant on the fitness of the customers.

Usage vs Age

The heatmap shows low correlation between Usage and Age of 0.015.

cardiodata_ndf[['Usage','Age']].corr()
  

	Usage	Age
Usage	1.000	0.015
Age	0.015	1.000

#scatter plot and lmplot to graphically visualize correlation
  maintitle = "Age vs Usage"
  #fig_no = 46

  sns.scatterplot(data = cardiodata_ndf, x = 'Age',y='Usage')
  plt.suptitle("Fig "+str(fig_no)+"- "+maintitle,fontsize=15)
  plt.title(subtitle_scatter)
  plt.show()

  sns.lmplot(data=cardiodata_ndf, x='Age',y="Usage", height=4, aspect=1.4)
  plt.title(subtitle_lmplot)
  plt.xlim(cardiodata_ndf['Age'].min()-1,cardiodata_ndf['Age'].max()+1)
  plt.show()

  fig_no+=1
  

The plot shows that there is no correlation (low correlation) between Age and Usage. That means that age is not a factor in the number of times a customer intends to use the treadmill.

Education vs Age

From the heat map, the correlation is 0.28, Low (below +/- 0.29). What we may expect to believe: older people have higher education levels.

#scatter plot and lmplot to graphically visualize correlation
  maintitle = "Age vs Education"
  fig_no = 47

  sns.scatterplot(data = cardiodata_ndf, x = 'Age',y='Education')
  plt.suptitle("Fig "+str(fig_no)+"- "+maintitle,fontsize=15)
  plt.title(subtitle_scatter)
  plt.show()

  sns.lmplot(data=cardiodata_ndf, x='Age',y="Education", height=4, aspect=1.4)
  plt.title(subtitle_lmplot)
  plt.xlim(cardiodata_ndf['Age'].min()-1,cardiodata_ndf['Age'].max()+1)
  plt.show()

  fig_no+=1
  

#Visualize education bins vs age
  #fig_no = 48

  sns.boxplot(data=cardiodata_ndf, x='Education_bins',y="Age")
  plt.title("Fig "+str(fig_no)+"- Boxplot Education bins vs Age",fontsize=15)
  plt.show()
  fig_no+=1
  

We cannot overwhelmingly conclude that older people have a higher education level. However, the outliers that represent older ages for higher degrees may point out that Masters and PhD degree holders are older than most other education level customers. However, considering the median values and distributions, we notice that the correlation may be indeed low.

Products

Models and Gender

#lets see the correlation between Models and Gender
  cardiodata_ndf[['Product','Gender']].corr()
  

	Product	Gender
Product	1.000	-0.231
Gender	-0.231	1.000

Low Correlation between Product Model and Gender.

#visualize all products vs gender in a countplot
  #fig_no = 49
  plt.title("Fig "+str(fig_no)+"- Product Preference by Gender", fontsize=15)
  sns.countplot(data=cardiodata_df,x="Product", hue='Gender')
  plt.show()
  fig_no+=1
  

#create dataframe to visualize products vs gender
  product_gender = cardiodata_df.groupby(['Product','Gender']).count().unstack()
  

#display Product vs gender (count)
  product_gender_sing= product_gender['Age']
  product_gender_sing
  

Gender	0	1
Product
0	40	40
1	31	29
2	33	7

#show Product vs Gender, separating Female and Male
  product_gender_TM798 = product_gender_sing.iloc[2]
  print(product_gender_TM798)

  product_gender_TM498 = product_gender_sing.iloc[1]
  print(product_gender_TM498)

  product_gender_TM195 = product_gender_sing.iloc[0]
  print(product_gender_TM195)
  

Gender
  0    33
  1     7
  Name: 2, dtype: int64
  Gender
  0    31
  1    29
  Name: 1, dtype: int64
  Gender
  0    40
  1    40
  Name: 0, dtype: int64
  

#visualize gender distribution per model
  #fig_no = 50

  labels_gen = ['Female', 'Male']
  plt.title("Fig "+str(fig_no)+"- TM798 Customer Gender Distribution", fontsize = 15)
  plt.pie(data=product_gender_sing,x=product_gender_TM798, autopct="%.1f%%",labels= labels_gen, pctdistance=0.5)
  plt.show()
  fig_no+=1

  plt.title("Fig "+str(fig_no)+"- TM498 Customer Gender Distribution", fontsize = 15)
  plt.pie(data=product_gender_sing,x=product_gender_TM498, autopct="%.1f%%",labels= labels_gen, pctdistance=0.5)
  plt.show()
  fig_no+=1

  plt.title("Fig "+str(fig_no)+"- TM195 Customer Gender Distribution", fontsize = 15)
  plt.pie(data=product_gender_sing,x=product_gender_TM195, autopct="%.1f%%",labels= labels_gen, pctdistance=0.5)
  plt.show()

  fig_no+=1
  

#stacked plot gender per module
  #fig_no = 53

  product_gender_sing.plot(kind='bar',stacked=True, color=['blue','orange'])
  plt.title("Fig "+str(fig_no)+"- Product Model Preference by Gender", fontsize = 14)
  plt.show()

  fig_no+=1
  

Model TM798 is highly preferred by Male customers.

Model TM195 is evenly preferred by Male and Female customers.

Model TM498 is slightly preferred by Male Customers.

Models and Marital Status

#Check correlation between Product Models and Marital Status
  cardiodata_ndf[['Product','MaritalStatus']].corr()
  

	Product	MaritalStatus
Product	1.000	-0.018
MaritalStatus	-0.018	1.000

Product Model and Marital Status have a low correlation.

#group products by marital status and store in dataframe
  product_maritalstatus = cardiodata_df.groupby(['Product','MaritalStatus']).count().unstack()
  

#show whats in the dataframe
  product_maritalstatus_sing= product_maritalstatus['Age']
  product_maritalstatus_sing
  

MaritalStatus	0	1
Product
0	32	48
1	24	36
2	17	23

#visual display in a counplot product preference by marital status
  #fig_no = 54

  plt.title("Fig "+str(fig_no)+"- Products Preference by Marital Status", fontsize=15)
  sns.countplot(data=cardiodata_df,x="Product", hue='MaritalStatus')
  plt.show()
  fig_no+=1
  

#count number of single and partnered customers per product preference
  product_maritalstatus_TM798 = product_maritalstatus_sing.iloc[2]
  print(product_gender_TM798)

  product_maritalstatus_TM498 = product_maritalstatus_sing.iloc[1]
  print(product_maritalstatus_TM498)

  product_maritalstatus_TM195 = product_maritalstatus_sing.iloc[0]
  print(product_maritalstatus_TM195)
  

Gender
  0    33
  1     7
  Name: 2, dtype: int64
  MaritalStatus
  0    24
  1    36
  Name: 1, dtype: int64
  MaritalStatus
  0    32
  1    48
  Name: 0, dtype: int64
  

#visual display distribution of prooduct prefeence by marital status
  #fig_no = 55

  labels_maritalstatus = ['Single', 'Partnered']
  plt.title("Fig "+str(fig_no)+"- TM798 Customer Marital Status Distribution", fontsize = 15)
  plt.pie(data=product_maritalstatus_sing,x=product_gender_TM798, autopct="%.1f%%",labels= labels_maritalstatus, pctdistance=0.5)
  plt.show()
  fig_no+=1

  plt.title("Fig "+str(fig_no)+"- TM498 Customer Marital Status Distribution", fontsize = 15)
  plt.pie(data=product_maritalstatus_sing,x=product_gender_TM498, autopct="%.1f%%",labels= labels_maritalstatus, pctdistance=0.5)
  plt.show()
  fig_no+=1

  plt.title("Fig "+str(fig_no)+"- TM195 Customer Marital Status Distribution", fontsize = 15)
  plt.pie(data=product_maritalstatus_sing,x=product_gender_TM195, autopct="%.1f%%",labels= labels_maritalstatus, pctdistance=0.5)
  plt.show()
  fig_no+=1
  

TM798 is overwhelmingly preferred by partnered customers

TM498 is slightly preferred by partnered customers

TM195 is evenly preferred by partnered and single customers

Models and Education

#Check correlation between Product Models and Education
  cardiodata_ndf[['Product','Education']].corr()
  

	Product	Education
Product	1.000	0.495
Education	0.495	1.000

There is a high correlation between Product Model and Education.

#education vs product boxplot
  #fig_no = 58
  sns.boxplot(x='Product',y='Education',data=cardiodata_df,palette='rainbow')
  plt.title("Fig "+str(fig_no)+"- Product Preference by Education Level", fontsize=15)
  plt.show()
  fig_no+=1
  

TM798 (Product 2) is preferred by customers with more education.

#Product preference visualizatoon by Education bins
  #fig_no = 59
  plt.title("Fig "+str(fig_no)+"- Product Preference by Education Level", fontsize=15)
  sns.countplot(data=cardiodata_df,y="Product",hue="Education_bins")
  plt.show()
  fig_no+=1
  

Customers of TM798 have higher education than customers that prefer other models

Models and Fitness

#correlation between model and fitness
  cardiodata_ndf[['Product','Fitness']].corr()
  

	Product	Fitness
Product	1.000	0.595
Fitness	0.595	1.000

There is a high Correlation between Product Model and Fitness.

#fitness vs product
  #fig_no = 60
  sns.boxplot(x='Product',y='Fitness',data=cardiodata_df,palette='rainbow')
  plt.title("Fig "+str(fig_no)+"- Product Preference by Fitness", fontsize=15)
  plt.show()
  fig_no+=1
  

This plot actually means that Product 2 (TM798) customers are on the edge of Fitness (5.0 means in this case Very Unfit and Unfit). Product 1 (TM195) and Product 2 (TM498) are at 3.0 Fitness (Average).

#Product vs Fitness bins
  #fig_no = 61
  plt.title("Fig "+str(fig_no)+"- Products Preference by Fitness Level", fontsize=15)
  sns.countplot(data=cardiodata_df,y = "Product", hue="Fitness_bins")

  plt.show()
  fig_no+=1
  

Customers that are very unfit prefer TM798 Customers that are average fit prefer TM195 Customers that are very fit prefer TM195

Models and Age

#correlation between model and age
  cardiodata_ndf[['Product','Age']].corr()
  

	Product	Age
Product	1.000	0.032
Age	0.032	1.000

Very Low Correlation

#age vs product boxplot to see distribution
  #fig_no = 62
  sns.boxplot(x='Product',y='Age',data=cardiodata_df,palette='rainbow')
  plt.title("Fig "+str(fig_no)+"- Product Preference by age",fontsize=15)
  plt.show()
  fig_no+=1
  

We may not be able to conclude much from the figure above. All products have very similar median ages.

#Trend of All Product preference by age
  fig_no = 63
  sns.displot(data=cardiodata_df, x = "Age", hue='Product', kind = 'kde')
  plt.title("Fig "+str(fig_no)+"- Treadmills preference by Age", fontsize=15)
  plt.show()
  fig_no+=1
  

TM195 is preferred by customers of all ages over the other models.

Models and Miles

#miles vs product
  #fig_no = 64
  sns.boxplot(x='Product',y='Miles',data=cardiodata_df,palette='rainbow')
  plt.title("Fig "+str(fig_no)+"- Product Preference by Miles", fontsize=15)
  plt.show()
  fig_no+=1
  

Customers that say that they will run most Miles, prefer TM798. Customers that say they will run less Miles, prefer TM195.

Models and Income

#correlation between model and income
  cardiodata_ndf[['Product','Income']].corr()
  

	Product	Income
Product	1.000	0.624
Income	0.624	1.000

High Correlation between Product Model and Income.

#income vs product
  #fig_no = 65
  sns.boxplot(x='Product',y='Income',data=cardiodata_df,palette='rainbow')
  plt.title("Fig "+str(fig_no)+"- Product Preference by income", fontsize=15)
  plt.show()
  fig_no+=1
  

Customers with higher incomes prefer TM798. Customers that have lower incomes prefer TM195.

#list of customers that earn more than 80,000
  cardiodata_df.loc[cardiodata_df['Income']>80000]
  

	Product	Age	Gender	Education	MaritalStatus	Usage	Fitness	Income	Miles	Age_bins	Education_bins	college_bins	Fitness_bins	Income_bins	Miles_bins
159	2	27	0	16	1	4	5	83416	160	21-30	College	College	Very Unfit	80-100K	One-Off Marathon
160	2	27	0	18	0	4	3	88396	100	21-30	Masters	College	Average	80-100K	Determined
161	2	27	0	21	1	4	4	90886	100	21-30	PhD	College	Unfit	80-100K	Determined
162	2	28	1	18	1	6	5	92131	180	21-30	Masters	College	Very Unfit	80-100K	One-Off Marathon
164	2	28	0	18	0	6	5	88396	150	21-30	Masters	College	Very Unfit	80-100K	One-Off Marathon
166	2	29	0	14	1	7	5	85906	300	21-30	Associate	College	Very Unfit	80-100K	Club
167	2	30	1	16	1	6	5	90886	280	21-30	College	College	Very Unfit	80-100K	Club
168	2	30	0	18	1	5	4	103336	160	21-30	Masters	College	Unfit	+100K	One-Off Marathon
169	2	30	0	18	1	5	5	99601	150	21-30	Masters	College	Very Unfit	80-100K	One-Off Marathon
170	2	31	0	16	1	6	5	89641	260	31-40	College	College	Very Unfit	80-100K	Club
171	2	33	1	18	1	4	5	95866	200	31-40	Masters	College	Very Unfit	80-100K	One-Off Marathon
172	2	34	0	16	0	5	5	92131	150	31-40	College	College	Very Unfit	80-100K	One-Off Marathon
173	2	35	0	16	1	4	5	92131	360	31-40	College	College	Very Unfit	80-100K	Fast Club
174	2	38	0	18	1	5	5	104581	150	31-40	Masters	College	Very Unfit	+100K	One-Off Marathon
175	2	40	0	21	0	6	5	83416	200	31-40	PhD	College	Very Unfit	80-100K	One-Off Marathon
176	2	42	0	18	0	5	4	89641	200	40+	Masters	College	Unfit	80-100K	One-Off Marathon
177	2	45	0	16	0	5	5	90886	160	40+	College	College	Very Unfit	80-100K	One-Off Marathon
178	2	47	0	18	1	4	5	104581	120	40+	Masters	College	Very Unfit	+100K	Determined
179	2	48	0	18	1	4	5	95508	180	40+	Masters	College	Very Unfit	80-100K	One-Off Marathon

Notice that all of them prefer TM798.

---

Model Analysis

TM798

#lets analyze TM798
  modelno = "TM798"
  tm_df = cardiodata_df.loc[cardiodata_df["Product"]== 2]
  

#Common declarations
  title = "Customer Preference by Age Groups"
  labelx = "Number of Customers"
  labely = "Age Group"
  ay = "Age_bins"
  

#TM798 vs Age
  #fig_no = 65
  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()

  fig_no+=1
  

Age group that prefer TM798 the most is 21-30.

#TM798 vs Age (Gender)
  fig_no = 66
  #hue declaration
  hueselect = "Gender"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  fig_no+=1
  

#TM798 vs Age (Education_bins)
  fig_no = 67
  #hue declaration
  hueselect = "Education_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()

  fig_no+=1
  

#TM798 vs Age (MaritalStatus)
  fig_no=68
  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Age (Usage)
  fig_no=69
  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Age (Fitness_bins)
  fig_no=70
  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Age (Income_bins)
  fig_no = 71
  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Age (Miles_bins)
  fig_no = 72
  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#Common declarations
  title = "Customer Preference by Gender"
  labelx = "Number of Customers"
  labely = "Gender"
  ay = "Gender"
  

#TM798 vs Gender (Age_bins)
  fig_no= 73
  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Gender (Education)
  fig_no = 74
  #hue declaration
  hueselect = "Education"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Gender (MaritalStatus)
  fig_no = 75
  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Gender (Usage)
  fig_no = 76
  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Gender (Fitness_bins)
  fig_no = 77
  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Gender (Income_bins)
  fig_no = 78
  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Gender (Miles bins)
  fig_no = 79
  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#Common declarations
  title = "Customer Preference by Education Level"
  labelx = "Number of Customers"
  labely = "Education Level"
  ay = "Education_bins"
  

#TM798 vs Education (Age_bins)
  fig_No = 80
  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Education (Gender)
  fig_no = 80
  #hue declaration
  hueselect = "Gender"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Education (Marital Status)
  fig_no = 81
  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Education (Usage_)
  fig_no = 82
  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Education (Fitness_bins)
  fig_no = 83
  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Education (Income_bins)
  fig_no = 84
  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Education (Miles_bins)
  fig_no = 85
  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#Common declarations
  title = "Customer Preference by Marital Status"
  labelx = "Number of Customers"
  labely = "MaritalStatus"
  ay = "MaritalStatus"
  

#TM798 vs Gender (Age bins)
  fig_no = 86
  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Marital Status (Gender)
  fig_no=87
  #hue declaration
  hueselect = "Gender"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Marital Status (Education_bins)
  fig_no = 88
  #hue declaration
  hueselect = "Education_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Marital Status
  fig_no = 89
  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Marital Status (Usage)
  fig_no = 90
  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Marital Status (Fitness_bins)
  fig_no = 91
  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Marital Status (Income_bins)
  fig_no = 92
  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Marital Status (Miles_bins)
  fig_no = 93
  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='center right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#Common declarations
  title = "Customer Preference by Usage"
  labelx = "Number of Customers"
  labely = "Usage (Times/week)"
  ay = "Usage"
  

#TM798 vs Usage (Age_bins)
  fig_no+=1
  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Usage (Gender)
  fig_no+=1
  #hue declaration
  hueselect = "Gender"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Usage (Marital Status)
  fig_no+=1
  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Usage (Usage_)
  fig_no+=1
  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Usage (Fitness_bins)
  fig_no+=1
  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Usage (Income_bins)
  fig_no+=1
  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Usage (Miles_bins)
  fig_no+=1
  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#Common declarations
  title = "Customer Preference by Fitness Level"
  labelx = "Number of Customers"
  labely = "Fitness Level"
  ay = "Fitness_bins"
  

#TM798 vs Fitness Level (Age_bins)
  fig_no+=1
  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Fitness (Gender)
  fig_no+=1
  #hue declaration
  hueselect = "Gender"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Fitness Level (Marital Status)
  fig_no+=1
  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Fitness Level (Usage_)
  fig_no+=1
  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Fitness Level (Fitness_bins)
  fig_no+=1
  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Fitness Level (Income_bins)
  fig_no+=1
  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Fitness Level (Miles_bins)
  fig_no+=1
  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#Common declarations
  title = "Customer Preference by Income Level"
  labelx = "Number of Customers"
  labely = "Income Level"
  ay = "Income_bins"
  

#TM798 vs Income (Age_bins)
  fig_no+=1
  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Income (Gender)
  fig_no+=1
  #hue declaration
  hueselect = "Gender"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Income (Marital Status)
  fig_no+=1
  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Income (Usage_)
  fig_no+=1
  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Income (Fitness_bins)
  fig_no+=1
  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Income (Income_bins)
  fig_no+=1
  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Income (Miles_bins)
  fig_no+=1
  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#Common declarations
  title = "Customer Preference by Miles"
  labelx = "Number of Customers"
  labely = "Miles"
  ay = "Miles_bins"
  

#TM798 vs Miles (Age_bins)
  fig_no+=1
  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Miles (Gender)
  fig_no+=1
  #hue declaration
  hueselect = "Gender"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Miles (Marital Status)
  fig_no+=1
  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Miles (Usage_)
  fig_no+=1
  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Miles (Fitness_bins)
  fig_no+=1
  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Miles (Income_bins)
  fig_no+=1
  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM798 vs Miles (Miles_bins)
  fig_no+=1
  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle("FIG "+str(fig_no)+" - " +modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

We can do the same for the other 2 treadmill models.

---

TM498

#lets analyze TM498
  modelno = "TM498"
  tm_df = cardiodata_df.loc[cardiodata_df["Product"]== 1]
  

#Common declarations
  title = "Customer Preference by Age Groups"
  labelx = "Number of Customers"
  labely = "Age Group"
  ay = "Age_bins"
  

#TM498 vs Age

  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Age (Gender)

  #hue declaration
  hueselect = "Gender"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Age (Education_bins)

  #hue declaration
  hueselect = "Education_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Age (MaritalStatus)

  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Age (Usage)

  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Age (Fitness_bins)

  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Age (Income_bins)

  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Age (Miles_bins)

  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#Common declarations
  title = "Customer Preference by Gender"
  labelx = "Number of Customers"
  labely = "Gender"
  ay = "Gender"
  

#TM498 vs Gender (Age_bins)

  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Gender (Education)

  #hue declaration
  hueselect = "Education"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Gender (MaritalStatus)

  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Gender (Usage)

  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Gender (Fitness_bins)

  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Gender (Income_bins)

  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Gender (Miles bins)

  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#Common declarations
  title = "Customer Preference by Education Level"
  labelx = "Number of Customers"
  labely = "Education Level"
  ay = "Education_bins"
  

#TM498 vs Education (Age_bins)

  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Education (Gender)

  #hue declaration
  hueselect = "Gender"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Education (Marital Status)

  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Education (Usage_)

  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Education (Fitness_bins)

  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Education (Income_bins)

  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Education (Miles_bins)

  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#Common declarations
  title = "Customer Preference by Marital Status"
  labelx = "Number of Customers"
  labely = "MaritalStatus"
  ay = "MaritalStatus"
  

#TM498 vs Gender (Age bins)

  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Marital Status (Gender)

  #hue declaration
  hueselect = "Gender"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Marital Status (Education_bins)

  #hue declaration
  hueselect = "Education_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Marital Status

  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Marital Status (Usage)

  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Marital Status (Fitness_bins)

  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Marital Status (Income_bins)

  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and _df, y = ay, ax=ax, hue=hueselect)
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Marital Status (Miles_bins)

  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='center right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#Common declarations
  title = "Customer Preference by Usage"
  labelx = "Number of Customers"
  labely = "Usage (Times/week)"
  ay = "Usage"
  

#TM498 vs Usage (Age_bins)

  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Usage (Gender)

  #hue declaration
  hueselect = "Gender"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Usage (Marital Status)

  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Usage (Usage_)

  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Usage (Fitness_bins)

  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Usage (Income_bins)

  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Usage (Miles_bins)

  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#Common declarations
  title = "Customer Preference by Fitness Level"
  labelx = "Number of Customers"
  labely = "Fitness Level"
  ay = "Fitness_bins"
  

#TM498 vs Fitness Level (Age_bins)

  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Fitness (Gender)

  #hue declaration
  hueselect = "Gender"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Fitness Level (Marital Status)

  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Fitness Level (Usage_)

  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Fitness Level (Fitness_bins)

  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Fitness Level (Income_bins)

  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Fitness Level (Miles_bins)

  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#Common declarations
  title = "Customer Preference by Income Level"
  labelx = "Number of Customers"
  labely = "Income Level"
  ay = "Income_bins"
  

#TM498 vs Income (Age_bins)

  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Income (Gender)

  #hue declaration
  hueselect = "Gender"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Income (Marital Status)

  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Income (Usage_)

  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Income (Fitness_bins)

  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Income (Income_bins)

  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Income (Miles_bins)

  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#Common declarations
  title = "Customer Preference by Miles"
  labelx = "Number of Customers"
  labely = "Miles"
  ay = "Miles_bins"
  

#TM498 vs Miles (Age_bins)

  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Miles (Gender)

  #hue declaration
  hueselect = "Gender"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Miles (Marital Status)

  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Miles (Usage_)

  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Miles (Fitness_bins)

  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Miles (Income_bins)

  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM498 vs Miles (Miles_bins)

  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

---

TM195

#lets analyze TM195
  modelno = "TM195"
  tm_df = cardiodata_df.loc[cardiodata_df["Product"]== 0]
  

#Common declarations
  title = "Customer Preference by Age Groups"
  labelx = "Number of Customers"
  labely = "Age Group"
  ay = "Age_bins"
  

#TM195 vs Age

  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Age (Gender)

  #hue declaration
  hueselect = "Gender"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Age (Education_bins)

  #hue declaration
  hueselect = "Education_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

In the age Group of 21-30, most customers have a College degree or an Associate degree.

#TM195 vs Age (MaritalStatus)

  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Age (Usage)

  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Age (Fitness_bins)

  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Age (Income_bins)

  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Age (Miles_bins)

  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#Common declarations
  title = "Customer Preference by Gender"
  labelx = "Number of Customers"
  labely = "Gender"
  ay = "Gender"
  

#TM195 vs Gender (Age_bins)

  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Gender (Education)

  #hue declaration
  hueselect = "Education"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Gender (MaritalStatus)

  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Gender (Usage)

  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Gender (Fitness_bins)

  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Gender (Income_bins)

  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Gender (Miles bins)

  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='lower right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#Common declarations
  title = "Customer Preference by Education Level"
  labelx = "Number of Customers"
  labely = "Education Level"
  ay = "Education_bins"
  

#TM195 vs Education (Age_bins)

  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Education (Gender)

  #hue declaration
  hueselect = "Gender"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Education (Marital Status)

  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Education (Usage_)

  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Education (Fitness_bins)

  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Education (Income_bins)

  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Education (Miles_bins)

  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#Common declarations
  title = "Customer Preference by Marital Status"
  labelx = "Number of Customers"
  labely = "MaritalStatus"
  ay = "MaritalStatus"
  

#TM195 vs Gender (Age bins)

  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Marital Status (Gender)

  #hue declaration
  hueselect = "Gender"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Marital Status (Education_bins)

  #hue declaration
  hueselect = "Education_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Marital Status

  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Marital Status (Usage)

  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Marital Status (Fitness_bins)

  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Marital Status (Income_bins)

  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Marital Status (Miles_bins)

  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='center right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#Common declarations
  title = "Customer Preference by Usage"
  labelx = "Number of Customers"
  labely = "Usage (Times/week)"
  ay = "Usage"
  

#TM195 vs Usage (Age_bins)

  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Usage (Gender)

  #hue declaration
  hueselect = "Gender"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Usage (Marital Status)

  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Usage (Usage_)

  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Usage (Fitness_bins)

  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Usage (Income_bins)

  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Usage (Miles_bins)

  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#Common declarations
  title = "Customer Preference by Fitness Level"
  labelx = "Number of Customers"
  labely = "Fitness Level"
  ay = "Fitness_bins"
  

#TM195 vs Fitness Level (Age_bins)

  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Fitness (Gender)

  #hue declaration
  hueselect = "Gender"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Fitness Level (Marital Status)

  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Fitness Level (Usage_)

  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Fitness Level (Fitness_bins)

  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Fitness Level (Income_bins)

  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Fitness Level (Miles_bins)

  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#Common declarations
  title = "Customer Preference by Income Level"
  labelx = "Number of Customers"
  labely = "Income Level"
  ay = "Income_bins"
  

#TM195 vs Income (Age_bins)

  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Income (Gender)

  #hue declaration
  hueselect = "Gender"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Income (Marital Status)

  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Income (Usage_)

  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Income (Fitness_bins)

  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Income (Income_bins)

  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Income (Miles_bins)

  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#Common declarations
  title = "Customer Preference by Miles"
  labelx = "Number of Customers"
  labely = "Miles"
  ay = "Miles_bins"
  

#TM195 vs Miles (Age_bins)

  #hue declaration
  hueselect = "Age_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Miles (Gender)

  #hue declaration
  hueselect = "Gender"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Miles (Marital Status)

  #hue declaration
  hueselect = "MaritalStatus"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Miles (Usage_)

  #hue declaration
  hueselect = "Usage"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Miles (Fitness_bins)

  #hue declaration
  hueselect = "Fitness_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Miles (Income_bins)

  #hue declaration
  hueselect = "Income_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

#TM195 vs Miles (Miles_bins)

  #hue declaration
  hueselect = "Miles_bins"

  #sets the size of the plot
  fig, ax = plt.subplots(figsize = (10,7))

  #sets the plot type and properties
  sns.countplot(data=tm_df, y = ay, ax=ax, hue=hueselect)

  #displays the main title of the plot and properties
  plt.suptitle(modelno + " : " + title, fontsize=15, fontweight='bold', family='monospace')

  #displays the sub-title of the plot and properties
  plt.title("hue "+hueselect)

  #puts the legend in the upper right corner
  plt.legend(loc='upper right', title=hueselect)

  #displays the x axis label we defined
  plt.xlabel(labelx)

  #displays the y axis label we defined
  plt.ylabel(labely)

  #displays the plot
  plt.show()
  

Multivariate Analysis

#import 3D visualization library
  import plotly.express as px
  

#plot scatter 3D

  fig_3d = px.scatter_3d(cardiodata_df,x='Product',y='Income',z='Usage')
  fig_3d.show()

  #Note: 0: TM195, 1: TM498, 2: TM798
  

here we can see that Customers that say they will use the machine more times a week, prefer Product 2 TM798.

#plot 3D scatter plot between miles, income, product
  fig_3d = px.scatter_3d(cardiodata_df,x='Product',y='Income',z='Miles')
  fig_3d.show()
  #Note: 0: TM195, 1: TM498, 2: TM798
  

you can see that Product 2 TM798 is the one customers with the highest income prefer.

#plot 3D Scatter plot between Education, Income and Product
  fig_3d = px.scatter_3d(cardiodata_df,x='Product',y='Income',z='Education')
  fig_3d.show()
  #Note: 0: TM195, 1: TM498, 2: TM798
  

You can see that Product 2 TM798 is the one customers with the highest level of education prefer.

#plot 3D Catter between Fitness, Education and Product
  fig_3d = px.scatter_3d(cardiodata_df,x='Product',y='Fitness',z='Education')
  fig_3d.show()
  #Note: 0: TM195, 1: TM498, 2: TM798
  

Here the customers that are most unfit (5), most educated, prefer product 2 TM798.

3D scatter plots allow us to see the relationships between m3 features.

Seaborn Multivariable Relationships

#produce seaborn pairplots
  sns.pairplot(data=cardiodata_ndf, hue='Product', size=2.5);
  

Conclusions and Recommendations

Customer Profile of Cardio Shop

General Profile

The profile of Good Cardio Fitness customer is:

• Young, mostly in the age group 21-30
• Mostly Male
• Mostly Partnered
• College Educated
• Average Fit
• Use of 3 times/week
• Low Middle Income (30-50K)
• Runner (miles) is intermediate

1. Customer Profile per Customer

The profile of customers per models:

TM195

• Most Popular
• Young, 21-30 age group
• Equally Popular between male and female customers
• Could be Partnered or Single customers
• College Educated
• Uses the treadmill 3 times per week
• Income is medium low (30-50K)
• Runner type (miles) is Intermediate

TM498

• Second most popular
• Young, 21-30 age group
• Equally Popular between male and female customers
• Could be Partnered or Single customers
• College Educated
• Uses the treadmill 3 times per week
• Income is medium low (30-50K)
• Runner type (miles) is Intermediate

TM798

• Least popular
• Young, 21-30 age group
• Overwhelmingly Male customers
• Partnered customers
• Popular with Master Degree customers or higher
• Mostly very unfit customers
• Uses the treadmill 4 times per week
• Higher income than all others (50 - 100K)
• Runner type (miles) is one-off marathon (pretendes to run more than others)

Insights

• Customer's Age, Gender, Marital Status are not important factors to determine model customer model preference.
• Income is the most important factor that determines customer model preference.
• Fitness, Miles (Runner Type), Usage, and Education are other important factors that influence the customer's model preference.
• Treadmill Customers are college educated.
• Most customers that buy a treadmill, may use it with a partner.
• The TM798 is the most expensive model of the three, and the TM195, the least expensive.
• TM798 is preferred by very unfit customers.
• The most educated customers prefer the TM798.
• Very unfit and unfit customers project to run more miles (runner type: One-off marathon), and say that they will use the machine most times a week.
• Customers with lower incomes prefer the TM195.
• There is a 11.1% increase on sales from the cheapest model, to the intermediate model and also 11.1% from the intermediate model and the most expensive model. This can't be just a coincidence. Further analysis may show that this may be linked to a model sales quota.

Recommendations

1. Offer the TM798 to the customers with the highest income, better education, and those who are most unfit. Customers that are most unfit seem to believe that buying the TM798 which we have concluded that it is the most expensive model, will give them an edge.

2. Although all models are bought mostly by customers in the age group 21-30, if we look past beyond the correlation matrix value, we also will notice that customers past their 40s seem to prefer TM798 over other models. So if a customer in their 40's inquires for a treadmill offer the TM798.

3. Offer the TM195 to customers in the lower income bracket, and to those that are average fit, it will be much easier to sell than trying to sell the TM798.

4. Search for customers with a college degree with income in the range from 80K-100Kyear. This is where the bulk of the sales should be and where there seems to be room to grow because the sales in this income group is small.

5. Although TM498 Outsold TM798, we recommend scrapping this model and focusing in only the TM195 and the TM798. Increase the price of the TM195 slightly to approximately make up the lost sales of the TM498 according to your forecast. Decrease the sales price of the TM798 so the income group of 50K-80K can buy this model instead.

6. If you have model sales quotas, try to set this only on the product that will give most profit, in this case apparently, the TM798.